Computer readable medium containing a set of computer readable instructions for grid-based insurance rating

ABSTRACT

A computer readable medium containing a set of computer readable instructions that when loaded into a computer configure that computer to: receive demographic data, fire station data, or other data associated with a location; receive a coordinate pair including a longitude and latitude of a location; determine a coordinate grid block bounded by latitude and longitude lines, which grid block is associated with the coordinate pair; query a database for a plurality of existing data associated with the coordinate grid block; and determine a rate based at least in part on the plurality of existing data and the demographic data, fire station data, or other data associated with a location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No.13/226,785 filed Sep. 7, 2011, which claims priority to U.S. ProvisionalPatent Application No. 61/381,885, filed on Sep. 10, 2010. The contentsof which are incorporated herein by reference in there entirety.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods forrating an insurance product. These rating systems and methods mayutilize computer, hardware, software, and data stores to gather andprocess data from grid locations near to a location of interest.

BACKGROUND

Insurance rating is the process of assessing the value of a given risk,and in turn pricing a policy to protect against that risk.Fundamentally, insurance premiums are designed to reflect the amount ofa payout should a covered event occur in view of the likelihood of theoccurrence of that event. The process of determining the cost of aninsurance policy is called rating. The rating process may include anumber of variables, including experience data for a specific insured,experience data for a class of insured entities, capital investmentpredictions, profit margin targets, and a wide variety of other datauseful for predicting the occurrence of certain real-world events aswell as the amount of damage likely to result from such events.

Further, experience rating involves analyzing past claims experience todetermine a prospective premium amount and/or a retrospective premiumadjustment. See, e.g., P. Booth, Modern Actuarial Theory and Practice,340-51 (Chapman & Hall/CRC 1999). For example, a business may operate alarge fleet of vehicles. And, that business may seek to insure thevehicles to cover property damage and to cover possible personal injuryclaims if a fleet vehicle were to be in an accident with anothervehicle. If the fleet is large enough or the business has been operatingthe fleet long enough, there may be enough historical data to reliablyand accurately estimate the expected claims for the next year. Thatestimate (possibly combined with an allocation of expenses or assessmentof an administrative fee) would represent the insurance premium in anideal scenario. At the end of the annual policy term, a surcharge orrefund may also be appropriate if the actual claims for the term werehigher or lower than the estimated claims amount.

A typical family seeking automobile insurance cannot, however, produceanywhere near the amount of data needed to make a reliable and accurateestimate of anticipated claims for their vehicle or vehicles. Thus,insurance companies must rate personal policies in a risk pool ofcomparable policies to produce enough data to make such an estimate. Onemechanism for doing this is to assess what data is available for thefamily (e.g., demographic information, types of vehicles, and whatlimited claim information is available) and use that data to assign anappropriate pool to the family.

The myriad types of data available to an insurer for performing therating process are often associated with geographic locations orregions. However, this association is not consistent or uniform. Someproperty crime data is associated with a “block” of addresses on a citystreet, e.g., 300-400 block of Main Street. Flood zone data and landelevation data may be stored as complex topographic maps. Lossexperience data may be associated with a coordinate pair representingthe longitude and latitude of the location of the loss event.

At present, insurance rating requires a complex search process tocompile relevant data for input into a rating function. For example, apolicy to be rated may be associated with a specific location, e.g., astreet address of a home or office or the location where a vehicle willbe parked at night. To rate a policy for that location, some subset ofthe relevant data must be gathered and provided to a rating algorithm.The gathering process is often computationally difficult in view of theinconsistent and non-uniform associations of data to geography discussedabove. In some instances, data is processed and aggregated by county,city, and/or postal ZIP code. This aggregation is made difficult by thenearly arbitrary boundaries defined by county lines, city limits, andZIP codes. Further, county, city, and postal ZIP code boundaries maychange over time. In other instances, data is processed by aggregatedsales territory.

Rates appropriate to each area are generally determined based on theassociated historical claims experience. While existing methods ofterritorial rating have served insurance providers well, theseapproaches can be problematic for several reasons: (1) geographicboundaries can change, as discussed above; (2) geographic areas may belarger than desired; (3) population may not be equally distributedwithin these geographic areas; and (4) historical claim experiencewithin these geographic areas may be limited.

SUMMARY

In accordance with the teachings of the present disclosure,disadvantages, and problems associated with existing insurance ratingsystems have been reduced.

According to one aspect of the invention, there is provided a method forrating insurance products using a programmed computer system. The methodcomprises receiving a coordinate pair comprising a longitude and alatitude of a location; determining a target coordinate grid blockbounded by latitude and longitude lines, wherein the coordinate gridblock encompasses the coordinate pair; querying a database for a targetset of existing data associated with the target coordinate grid block,wherein the existing data was associated with the target coordinate gridblock prior to receiving the coordinate pair; and calculating a purepremium based at least in part on analyzing the data in the target set.

According to another aspect of the invention, there is provided a methodfor rating insurance products using a programmed computer system. Themethod comprises receiving a coordinate pair comprising a longitude anda latitude of a location; determining a target coordinate grid block bytruncating a decimal representation of the longitude and latitude of thelocation; querying a database for a first set of existing dataassociated with the target coordinate grid block, wherein the existingdata was associated with the target coordinate grid block prior toreceiving the coordinate pair; setting a ring counter to an initialvalue; determining a current ring of coordinate grid blocks gridadjacent to and surrounding the target grid block; querying the databasefor a second set of existing data associated with each coordinate gridblock in the current ring of coordinate grid blocks; and calculating apure premium based on constant values and an analysis of the data in thefirst set and the second set.

According to a further aspect of the invention, there is provided acomputer system for rating insurance products. The computer systemcomprises a processor, a memory, and a set of computer readableinstructions stored in the memory. When executed by the processor, thecomputer readable instructions configured to receive a coordinate pairincluding a longitude and a latitude of a location; determine acoordinate grid block bounded by latitude and longitude lines, whichgrid block is associated with the coordinate pair; query a database fora plurality of existing data associated with the coordinate grid block;and calculate a pure premium based at least in part on the plurality ofexisting data.

According to yet another aspect of the invention, there is provided acomputer system for rating insurance products comprising a processor, amemory, an input for receiving a coordinate pair including a longitudeand a latitude of a location, a grid block determination means fordetermining a coordinate grid block bounded by latitude and longitudelines, which grid block is associated with the coordinate pair; an inputfor receiving database query results from a database comprising aplurality of existing data associated with the coordinate grid block;and a rating means for determining a rate based at least in part on theplurality of existing data.

According to still another aspect of the invention, there is provided acomputer readable medium containing a set of computer readableinstructions to be loaded into a computer. When those instructions areloaded into a computer, they configure that computer to receivedemographic data, fire station data, or other data associated with alocation; receive a coordinate pair including a longitude and latitudeof a location; determine a coordinate grid block bounded by latitude andlongitude lines, which grid block is associated with the coordinatepair; query a database for a plurality of existing data associated withthe coordinate grid block; and determine a rate based at least in parton the plurality of existing data and the demographic data, fire stationdata, or other data associated with a location.

According to yet another aspect of the invention, there is provided amethod for rating insurance products using a programmed computer system.The method comprises receiving a coordinate pair comprising a longitudeand a latitude of a location; determining a target coordinate grid blockbounded by latitude and longitude lines, which grid block is associatedwith the coordinate pair; querying a database for a first set ofexisting data associated with the target coordinate grid block, whereinthe existing data was associated with the target coordinate grid blockprior to receiving the coordinate pair; querying a database for a secondset of existing data associated with a regulator, which regulatesinsurance policies in a geographic area comprising the location; andcalculating a pure premium based on constant values and an analysis ofthe data in the first set and the second set.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features, and wherein:

FIG. 1 illustrates a stylized town map overlaid with a coordinate grid,according to certain embodiments of the present disclosure.

FIG. 2 illustrates a stylized neighborhood map with two differentgridline representations, according to certain embodiments of thepresent disclosure.

FIGS. 3A-B illustrate two variations of a process of gatheringprogressively larger amounts of geographically relevant data from GRIDcells.

FIG. 4 illustrates a stylized town map superimposed with rings accordingto certain embodiments of the present disclosure.

FIG. 5 illustrates the stylized town map overlaid with a coordinate gridand an alternative set of geographic boundaries, namely ZIP codes.

FIG. 6 illustrates various approaches to mapping data into a grid-basedrepresentation scheme.

FIGS. 7A-7B illustrate processes for coordinating data with a GRID mapaccording to an embodiment of the invention is disclosed according to aflow chart.

FIG. 7C illustrates an example of graphical information to be mappedinto a GRID-based data structure.

FIG. 8 provides a flow chart, which is illustrative of an embodiment ofthe invention.

FIG. 9A provides representative GRID distance weighting values, for anautomobile example.

FIG. 9B provides an overview of the methodology for an automobileexample.

FIG. 9C provides the GRID ring level data for an automobile example.

FIG. 9D provides the GRID cell level data for an automobile example.

FIG. 9E provides methodologies for calculating distance between twolatitude and longitude coordinate pairs in an automobile example.

FIG. 9F provides the results of the distance calculations for anautomobile example.

FIG. 10A provides representative GRID distance weighting values, for ahomeowner's policy example.

FIG. 10B provides an overview of the methodology for a homeowner'spolicy example.

FIG. 10C provides the GRID ring level data for a homeowner's policyexample.

FIG. 10D provides the GRID cell level data for a homeowner's policyexample.

FIG. 10E provides methodologies for calculating distance between twolatitude and longitude coordinate pairs in a homeowner's example.

FIG. 10F provides the results of the distance calculations for ahomeowner's example.

FIG. 11 illustrates a computing and information handling systemaccording to one embodiment of the invention.

DETAILED DESCRIPTION

Preferred embodiments and their advantages over the prior art are bestunderstood by reference to FIGS. 1-11 below. However, the presentdisclosure may be more easily understood in the context of a high leveldescription of certain embodiments.

As discussed in the BACKGROUND, insurance risks are often rated based ondata relevant to one or more particular geographic locations. Forexample, a homeowner's policy or commercial fire policy covers certainrisks to a physical structure at an identified geographic location. Therisks associated with that geographic location may or may not bedifferent than the risks associated with any other location. In oneillustrative example, the risk of fire may be lower in a neighborhood ofbrick homes than in a neighborhood of wooden homes. This lower risk mayrepresent a lower incidence of damage due to fire and/or a lower typicalclaim amount resulting from previous fires in that neighborhood. Inanother example, an automobile policy may represent a risk that variesrelative to the location where a car is parked at night and/or duringthe day. If the car is parked on the street in an urban neighborhood,the risk of damage may be significantly higher than if it is parked in asecure garage. For the purposes of this disclosure, the term targetlocation will be used to reference a specific geographic locationrelevant to rating of a specific covered risk. In some circumstances,there may be multiple relevant geographic locations (e.g., a garagelocation and a work location for an automobile), but the presentdisclosure will treat each location independently for the sake ofclarity.

One aspect of the invention is to define territories based on latitudeand longitude coordinates. This method allows insurance providers tomore finely segment policy pricing based on geographic characteristicsand provides more pricing points than current structures. For purposesof this application, the system is called Geographic RatingIdentification (GRID) Based Rating and utilizes latitude and longitudecoordinates to determine the Location Rating Factors (LRFs) that mayapply to a policy. The GRIDs may be set up so that the individual targetcells are not equal in size according to area. Rather, the latitudinaland longitudinal coordinates may be truncated at varying precisions toprovide individual target cells of different sizes according to area.For example, in urban, populated areas, the GRID may be smaller, whilein rural, less populated areas the GRIDs may be much larger. Once theGRIDs are established, then individual policies may be quickly assignedto GRIDs by looking up the latitudinal and longitudinal coordinates ofthe associated location address. Truncation of the latitudinal andlongitudinal coordinates may provide for a much quicker lookupfunctionality.

Latitude and longitude coordinates for particular address locations maybe obtained via a geocoding process or service, wherein the latitude andlongitude coordinates are provided with a precision to the sixth decimalplace for both values along with codes indicating the quality of theresult. Results at that level of precision identify a geographic pointat the sub-inch level.

Latitude and longitude coordinates may be used to define a coordinatesystem using four defined corners and developing a database table andlookup methodology, wherein each GRID cell area is of similar geographicsize. The complexity of the queries required to retrieve results fromthe developed tables makes this solution less preferred.

Alternatively, a database function may be used to group geographicallylike risks. Typically, these areas are not equal in geographic size. Thegrouping may use a single database function to provide significantlyfaster results. This method also could be easily integrated withexisting database systems used for the rating process and can moreeasily connect the pieces to form a countrywide map.

According to a further alternative, latitude and longitude coordinatesare used to define GRID cells of different sizes. Because the latitudeand longitude coordinates provide precision to the sixth decimal place,larger GRID cells may be defined by truncating the latitude andlongitude coordinates to fewer decimal places. Thus a single databasemay accommodate variable GRID cell sizes wherein larger GRID cells aredefined by latitude and longitude coordinates that have fewer decimalplaces and smaller GRID cells are defined by latitude and longitudecoordinates that have more decimal places.

Some benefits of GRID Based Rating include: (1) latitude and longitudecoordinates are fixed and do not change over time (unlike ZIP codes orother geographic boundaries); (2) rates are based on loss experience inthe target GRID cell and nearby GRID cells, as opposed to large areascovering multiple ZIP codes or counties with portions remote from thelocation of interest; (3) improved matching of price to risk overcurrent territorial rating; (4) promotes smoother transitions fromhigher cost areas to lower cost areas; and (5) can utilize data externalto insurer database information, such as historical meteorological data.

According to this disclosure, GRID cells are defined as non overlappinggeographic areas defined by truncated latitude and longitude values,wherein the size of the GRID cells can vary depending on the level oftruncation precision. A truncation example is provided that illustrateshow the level of truncation can be used to capture more or fewer pointlocations in a given GRID cell. The example in TABLE 1 has nine pointlocations that exist at the noted latitude and longitude coordinates.Each point is lettered and is accurate to six positions to the right ofthe decimal for both latitude and longitude.

TABLE 1 Point Name Latitude Longitude Premium A 42.221623 −80.241321 145B 42.224237 −80.248427 225 C 42.226772 −80.242043 412 D 42.229623−80.242843 299 E 42.228791 −80.247182 305 F 42.228423 −80.238221 205 G42.228911 −80.251822 335 H 42.230879 −80.249142 289 I 42.225291−80.236382 435

To develop a summarized amount of premium for a particular area (GRIDcell), the absolute value of latitude and longitude for each point aretruncated at two decimal positions and grouped in a query of thedatabase. These summaries are listed in TABLE 2.

TABLE 2 Point Name Latitude Longitude Premium A 42.22 80.24 145 B 42.2280.24 225 C 42.22 80.24 412 D 42.22 80.24 299 E 42.22 80.24 305 F 42.2280.23 205 I 42.22 80.23 435 G 42.22 80.25 335 H 42.23 80.24 289

With this level of truncation, the nine points fall into four differentGRID cells, which are bounded by two decimal point precision latitudeand longitude lines. Next, the points are grouped according to thetruncated latitude and longitude, as shown in TABLE 3.

TABLE 3 Latitude Longitude Total Premium 42.22 80.24 1386 42.22 80.23640 42.22 80.25 335 42.23 80.24 289

The latitude and longitude coordinates are components of an uniqueidentifier, GRID ID, which is used to identify each GRID cell. GRID IDis determined by first taking the absolute value of the latitude andlongitude coordinates, truncating the coordinates to a specified numberof digits, eliminating the decimal point, and finally concatenating theresulting values together. The specified digits include three digits tothe left of the decimal place in order to accommodate up to 180 degreesof longitude. The GRID ID matches the latitude and longitude of thelower right corner (the Southeast corner) of the GRID cell. Thefollowing transformation may be implemented in software as a grid blockdetermination means.

This example uses a single attribute to demonstrate the grouping, butincurred losses or other factors could also have been used. Thetruncation method can be used as part of an actuarial flow where totalpremium is compared to total losses and other factors to develop anappropriate rate for a grid area. The GRID ID may be represented as asingle numeric value, e.g., 0422208024, or may be represented by itscomponent parts, e.g., 04222 and 08024. While the examples provided showlatitude and longitude represented in decimal format, other formats canprovide the same function including, for example, degrees, minutes, andseconds.

At this point in the disclosure, reference to the figures may behelpful.

FIG. 1 illustrates a stylized town map overlaid with a coordinate grid,according to certain embodiments of the present disclosure. This mapprovides some context for the Map 100 includes a 7×9 array of coordinategridblocks, locations A-F, river 101, highway 102, surface streets 103,and neighborhoods 110, 111, and 112. The coordinate grid includesgridlines at hundredths of a degree from 30.420N to 30.429N and from97.270W to 97.277W. Note that a reference to a grid block at (X, Y) willrefer to the block with a lower right corner at coordinate (X, Y). Forexample, highway 102 crosses river 101 in grid block (97.272W, 30.425N).Locations A-F are each associated with a latitude/longitude coordinatepair. Each location A-F may represent a real property location to beinsured, a physical location where personal property may be garaged orkept, or the location of historical data point (e.g., claim or crimescene).

River 101 is a body of water separating neighborhood 111 from 110 and112. River 101 may represent other geological or geographical barrierslike steep inclines or ravines. River 101 may represent a rail line orlimited access roadway. These barriers may represent disparate data setswhere, for example, neighborhood 110 may represent a collection ofhomes. In contrast, neighborhood 112 may include a mix of commercial andresidential properties. Finally, neighborhood 111 may be an urban citycenter with high-rise offices and residences. When rating a homeowner'spolicy for location A, data from locations C, D, and E withinneighborhood 110 may be more relevant than data from a high-risecondominium building at location B. This may be the case even thoughlocation B is closer than locations C, D, and E. Moreover, even iflocation B is a single-family residence, the homes at locations A and Bmay be of significantly different character, and therefore less relevantto each other from an insurance rating perspective.

In some embodiments, river 101 may represent a political border (e.g.,between states or countries). Because insurance is regulated bypolitical entities, namely each of the fifty United States plus theDistrict of Columbia, data may be compartmentalized with a state'sborders. This compartmentalization may be for accounting purposes andmay be required by law or regulation.

Highway 102 is a major vehicle artery cutting through the center of thetown and spanning river 101. Highway 102 may present a similar barrieras river 101. Highway 102 may also provide access to emergency servicessuch as fire or police departments. Surface streets 103 are roadways,paths, or other means for surface transportation.

FIG. 2 illustrates a stylized neighborhood map with two differentgridline representations, according to certain embodiments of thepresent disclosure. Map 200 includes properties 201, roads 202, 203, and204, fire station 205, fire hydrant 206, equidistant gridlines 210, 211,212, and 213, and coordinate gridlines 214, 215, 216, and 217.

Properties 201 represent real estate structures subject to insurancerating, subject to insurance claims, and the locations forinsurance-related events. Properties 201 are illustrated as detached,single-family residences, but may be any sort of real property. Forexample, some of these properties may be multi-family properties such asduplexes, triplexes, or condominiums. Some properties may be adjoiningtown homes or row houses. Some properties may be mid-rise or high-riseapartments or condominiums. Some properties may be zoned forresidential, commercial, industrial, or municipal use. Some propertiesmay be unimproved land in a natural state or subject to some other use.Properties 201 need not be homogenous as a single-family detached homemay be located next to a condominium project and next to a retailestablishment.

Roads 202, 203, and 204 connect properties 201 to allow transportationof people, items, and vehicles between or past those properties. Roads202, 203, and 204 are illustrated as uniformly sized, paved, residentialroads, but may be any sort of road. Some roads may be paved withasphalt, brick, concrete, or stone. Some roads may be made from gravel.Some roads may be dirt roads. Some roads may be pedestrian walkways orbike paths. Some roads may include dedicates lanes for motor vehiclesand bicycles. Roads may have any number of lanes and may have paintedstripes demarking lanes of traffic. The intersections of roads 202, 203,and 204 may include traffic control devices such as yield signs, stopsigns, or traffic lights.

Fire station 205 and fire hydrant 206 represent insurance-relatedcommunity services. Other services may include police stations andhospitals. Proximity to insurance-related community services may affordsome protection against certain types of loss or may reduce the likelyimpact of certain events. For example, a nearby community police stationmay reduce the likelihood of certain property crimes. In anotherexample, a nearby fire station may reduce the response time foremergency medical or fire assistance, thereby reducing the likelihood ofmedical complications or total loss of a property to fire.

Equidistant gridlines 210, 211, 212, and 213 illustrate one datasegmentation approach, wherein a map is overlaid with a set of virtualgridlines spaced an equal distance from each other in a given direction.In this case, gridline 210 is 1000 m West of gridline 211 and gridline212 is 1000 m North of gridline 213. This one kilometer grid (or a onemile grid) provides a well understood and easy to illustratesegmentation of zones on a map. In particular, equidistant gridlines210, 211, 212, and 213 define GRID cell 220, which is roughly one squarekilometer. One drawback of the equidistant grid is that any data pointassociated with a coordinate pair—defined as (latitude, longitude)—mustbe mapped into this equidistant grid system prior to analysis with otherdata points within the grid block. For example, if property 201 atlocation (30.426N, 97.749W) is the subject of a rate quote, the ratingsystem must first determine that location coordinate pair falls withinGRID cell 220. Then the rating process may proceed.

Coordinate gridlines 214, 215, 216, and 217 illustrate another datasegmentation approach, wherein a map is overlaid with a set of gridlinesfollowing longitudinal and latitudinal lines. (The term coordinate gridis used here to refer to latitude/longitude grid used by, for example,the United States Geological Survey.) The coordinate gridlines 214 and215 follow the 97.75W and 97.74W longitudes, respectively. Thecoordinate gridlines 216 and 217 follow the 30.43N and 30.42N latitudes,respectively. These coordinate gridlines are spaced at two decimalplaces of the latitude/longitude coordinates, but other spacing may beappropriate as will be discussed below. In some embodiments, a variablespacing may be used wherein some data is accessed or processed accordingto a narrower spacing than other data. In certain embodiments, two ormore sets of coordinate gridlines may be used for certain types of dataor certain locations, e.g., where data density may vary significantly.In one example, property damage experience data may be dense in an urbanlocation while climate data may be relatively sparse, suggesting thatproperty damage experience data may be queried using a smallercoordinate block size than climate data. In another example, propertydamage experience data may be sparse in a rural farming communitysuggesting that damage experience data may be queried using a largercoordinate block size than in a dense urban situation.

The curvature of the illustrated coordinate gridlines is exaggerated toillustrate one major difference between this gridline system and theequidistant gridline system. Rather than form a square, the coordinategridlines 214, 215, 216, and 217 form an oblong GRID cell 230, roughly1100 meters tall following a longitudinal cross-section and roughly 960meters across following a latitudinal cross-section. These distances arecalculated based on the well-documented Haversine formula—though otherformulas may be employed. This oblong shape will be more pronounced athigher latitudes, e.g., at 64.72N in Fairbanks, Ak. where the sametwo-decimal grid block would be roughly 1110 meters by 480 meters. Insome embodiments, the coordinate gridlines used at such an extremenorthern latitude may be adjusted to more closely approximate a squaregrid block. This may be accomplished, for example, by defining a gridblock with corners at (64.72N, 147.48W) and (64.73N, 147.50W), orincrementing the latitude by 0.01 degrees while incrementing thelongitude by 0.02 degrees.

Data Aggregation in the Ratemaking Process

GRID Based Rating utilizes latitude and longitude coordinates toestablish a network of small territories or “GRID cells” across a stateor region. Each GRID cell represents a defined area, with corner pointsdefined in terms of truncated latitude and longitude. Each policy isassigned to a GRID cell based on its latitude and longitude coordinates,which are determined by using a reference's location address or theaddress where a vehicle is garaged.

The premium and loss experience used to calculate policy pricing may bederived from data associated with a target cell—or the GRID cell thatencompasses the target location as well as data associated withsurrounding GRID cells. The aggregate risk exposure is determined basedon the data associated with the target cell and the immediatelysurrounding GRID cells. Certain embodiments incorporate an iterativesearch process using progressively larger data sets with each data setincorporating data associated with GRID cells that are progressivelyfurther from the target cell. This method may ensure that the mostgeographically relevant data is included in the calculations for eachtarget location. GRID Based Rating uses this data to derive LocationRating Factors (LRFs) for each GRID cell.

FIGS. 3 a-b illustrate two variations of a process of gatheringprogressively larger amounts of geographically relevant data from GRIDcells. Both figures are oriented with the top of the page representinggeographic North and the right side of the page representing geographicEast.

FIG. 3 a illustrates a process of gathering progressively larger amountsof geographically relevant data using concentric rings around a targetGRID cell. FIG. 3 a includes a target cell, ring 1 surrounding andcontiguous with the target cell, and ring 2 surrounding and contiguouswith ring 1. The number of rings and the size of each ring may vary toaccommodate different data requirements, geographic anomalies, etc. Insome embodiments, each ring includes an equal number of grid cells alongboth the North/South cross-section as with the East/West cross-section.In other embodiments, more or fewer grid cells may be included across aNorth/South cross-section than are included across an East/Westcross-section in order to more closely represent equal geographicdistance in each direction. Two rings are illustrated, but more or fewerrings may be utilized. In some embodiments, the target cell may bereferred to as ring 0.

According to one aspect of the invention, there is a method having thefollowing steps: collecting data for the target cell and determiningwhether the current data collection has enough historical data toprovide actuarially credible results. If not, an iterative process isperformed including collecting data from rings of GRID cells immediatelysurrounding the cell(s) for which data has been collected and evaluatingat each iteration whether enough historical data has been collected.Once a credible set of data has been collected, distance weighting andcredibility weighting are applied and a pure premium is calculated.

FIG. 3 b illustrates an alternative process of gathering progressivelylarger amounts of geographically relevant data using concentric blocksaround a target cell. FIG. 3 b includes a target cell, block 1surrounding and centered on the target cell, and block 2 surrounding andcentered on the target cell. While blocks 1 and 2 are illustrated assquares, each may be a rectangle to compensate for rectangular GRIDblocks at high or low latitudes.

In certain embodiments, the iterative process first gathers data fromthe target cell and determines if additional data is needed to get acredible data set. If so, data is gathered from block 1 (includingduplicate data from the target cell) and a distance weighting isapplied. In certain embodiments, duplicate data may be explicitlyexcluded. In other embodiments, the duplicate data is left in the setfor computational efficiency and any skew is lessened through theapplication of the distance weight. This process iterates until asufficient amount of data has been collected to satisfy a credibilitythreshold.

In certain computational environments, the process illustrated in FIG. 3b may be preferable as it may be simpler and/or faster than the processillustrated in FIG. 3 a because data may be queried with two boundarypoints (representing a rectangle) rather than a series of boundarypoints representing the more circular rings illustrated in FIG. 3 a.

FIG. 4 illustrates a stylized town map superimposed with rings accordingto certain embodiments of the present disclosure. Map 400 illustratesthe same points of interest as FIG. 1 as well as target cell 401 andrings 402, 403, and 404. In this illustration, locations F and B eachrepresent high-rise residential buildings while locations A, C, D, and Erepresent single family homes. If the standard ring-based method isutilized on this data set, data associated with location C may carrymore weight in the calculation than data associated with location Bbecause location C is in the first ring (ring 402) and location B is inthe second ring (ring 403). The distance weighting will reduce theweight accorded to data associated with location B. Because location Bis more similar to location F than C, certain techniques may be appliedto filter the data based on relevancy criteria. In some embodiments,data may be associated with attributes. For example, a claim forwindstorm related roof damage a location C may be associated with anattribute of “single-family residence.” In another example, a claim forsmoke damage due to an electrical fire at location B may be associatedwith an attribute of “high-rise residence.” In some embodiments, thequery for data in a given cell or ring may include a Boolean filter torestrict results, e.g, “NOT ‘single-family residence’” or “AND‘high-rise residence.’” In some embodiments, a post processing step mayapply a lesser weight to results not associated with “high-riseresidence.”

Data Relevant to the Ratemaking Process

With an understanding of the generalized data gathering process, someelaboration is necessary as to the types of data relevant to ratemaking.In certain embodiments, a first step of the method is to collecthistorical data on a given target GRID cell and all nearby GRID cellswithin a specified radius. In this step, an exposure adjustment may alsobe applied by peril (e.g., fire, crime, and other extended coverage).Data may be collected from a variety of sources both internal andexternal to the insurance provider. For example, data may be collectedregarding fire station and fire hydrant locations and characteristics,weather data, government data, in particular census, tax, population,traffic, employment, businesses, crime statistics, soil, vegetation,flood planes, burn zones, etc. Telematic devices may also be attached tocars to collect use and driving data.

Census data may include: population density, average number of vehiclesper household, average travel time, and travel type (drive, car pool,public transportation, etc.). Census data may be obtained from thirdparty vendors or other external sources. Fire station data may include:distance to responding fire station, distance to nearest fire station,fire station type (paid, volunteer, combination, other), and firestation characteristics (trucks, equipment, water supply, etc.). FireStation data may be obtained from third party vendors or other externalsources. Crime data may include: robbery counts, burglary counts,larceny-theft counts, motor vehicle theft counts, and arson counts.Brush fire data may include: brush fire potential, and vegetation index.Weather data may include: average number of hail events, average hailstone size per event, average number of tornado events, average tornadorating per event, average tornado length per event, average tornadowidth per event, average annual rainfall, average annual snowfall,average high temperature, average low temperature, and frequency ofweather watches and warnings issued. Other data may include: trafficdensity, average driving distance, earth aspect (measures the amount ofsunlight at a location), slope, fault lines, and soil type.

Historical data may be collected from data sources that are bothinternal and external to an insurance company. In some cases, dataexternal to an insurance company may not be used because the data israndomly available within/across states and data may have limitedadditional explanatory value.

Unless otherwise specified, average and statewide values may be treatedas constants for the purposes of the present invention if those valuesremain constant for some period of time. In other words, if two policiesthat cover properties at two locations at opposite corners of a largestate are rated at roughly the same time, any average or statewide valuethat remains the same in both rating calculations will be referred toherein as a constant.

When data is collected from outside an insurance provider, the data mayneed to be mapped into the coordinate grid in order to speed lookup andsimplify the required database queries. For example, the outside datamay be initially associated with postal ZIP codes, street addresses,school districts, or any other geographic locations and regions.

FIG. 5 illustrates the stylized town map overlaid with a coordinate gridand an alternative set of geographic boundaries, namely ZIP codes. Map500 illustrates at least three ZIP codes, ZIP A is contained within map500, while ZIP B and ZIP C each appear to extend beyond the edges of map500. Map 500 illustrates how postal ZIP codes may not coincide with thecoordinate lat/long grid. In one example, postal ZIP A is shown tocompletely encompass a number of grid cells, e.g., the grid cellsencompassing each of locations C and D. ZIP A also includes portions ofa number of other grid cells, e.g., a portion of the grid cellencompassing location E.

If data is associated with a ZIP code, rather than a grid cell, agrid-based query will not retrieve that data. For example, assume thatZIP A is associated with negligible levels of flammable vegetation, thusreducing the risk of a loss due to fire relative to homes in areas ofhigher levels of flammable vegetation. ZIP A may cover an urban area,while ZIP B may abut a forest. The data associated with ZIP A will becalled the ZIP A Data. In order to perform a grid-based query, it isdesirable to associate the ZIP A Data with one or more grid cellscovered by ZIP A.

FIG. 6 illustrates various approaches to mapping data into a grid-basedrepresentation scheme. Specifically, FIG. 6 includes a zoomed inrepresentation of a specific grid cell including location E and includesfive separate abstract grid cells illustrating five means for mappingdata into a grid cell. Grid cell 601 includes grid boundaries atN30.424, N30.423, W97.277, and W97.276, and center point 602 located atN30.4235, W97.2765. A boundary line between ZIP A and ZIP D intersectsthe grid cell illustrated in map 601. The GRID ID for this cell is030423097276 assuming a GRID resolution of three decimal points. Gridcells 603-606 illustrate four example methods of mapping data into thegrid cell, origin mapping 603, center point averaging 604, four corneraveraging 605, five point averaging 606, and pixel counting 607.

Using origin mapping 603, a data value associated with ZIP A will beassociated with cell 030423097276 because the origin of this cell(marked by a single dot) is within ZIP A. Using center point averaging604 results in the same mapping because center point 602 is also in ZIPA.

Using four corner averaging 605, a numeric data value is retrieved foreach corner of the grid cell with the average of the four points savedas the representative attribute of cell 030423097276. For example, ifZIP A is associated with an average vegetation coverage of 0.25 and ZIPB is associated with an average vegetation coverage of 0.60, then theaverage is 0.43 rounded to two decimal places. That average number wouldthen be stored in the database in association with cell 030423097276.

Using five point averaging 606, a value is retrieved for the centerpoint and each of the corners. The average of the five values is thenassociated with 030423097276. Here, the average of vegetation coveragevalues of 0.25, 0.25, 0.25, 0.60, and 0.60 is 0.39.

Using pixel counting 607, a portion of a raster map (e.g., from agraphic image file) is superimposed on a grid cell and aligned. Theraster map may be a graphical representation of a topographical map orother graphically represented information. In this approach, eachnon-zero pixel within the grid cell boundaries is counted and theaverage coverage of the grid cell is applied to the value represented bythe raster map. For example, in pixel counting 607, a total of 35 out of120 pixels are shaded in the raster map. A total of 29 pixels 608(shaded grey), covering approximately 24% of the 120 pixels in the gridcell, may be associated with a mask value of 0.045. A total of 6 pixels609 (with crosshatching), covering 5% of the grid cell, may beassociated with a mask value of 0.820. As a result, the average maskvalue is (0.045*0.24)+(0.820*0.05), or 0.052. In some embodiments, aminimum or maximum mask value may be associated with the grid cell.

Referring to FIGS. 7 a-7 b, processes for coordinating data with a GRIDmap according to an embodiment of the invention is disclosed accordingto a flow chart. Geographic data in various formats are identified in701. The geographic data is being collected 702 from various sources.The formats may include Vector (.shp), Tabular (.csv), Raster (.tiff,.img). For the Vector data 703, a mathematical formula (e.g., usingmethods 603-606) may be applied 706 to compute a value for a grid basedon related data points contained within each GRID cell. Alternatively,the Vector images 703 may be converted 706 into Raster imagesassociating specific values to each pixel color (e.g., using method607). The collected tabular data 704 may be applied 707 to a Vectordefinition and then one can follow a Vector data path. Alternatively,the tabular data 704 may be mathematically derived so that the remainingundefined space between the base data points is obtained. The result ofthe process of step 707 may then be applied to step 706 as describedabove or to step 709 as described below. The Raster data 705 may bealigned 708 to the appropriate geographic projection. Next, both thealigned Raster data 705 and the applied tabular data 704 may be used todevelop a range of values by sampling data 709. The sample data may thenbe used to obtain average values by pixel and summarized at desired gridsize at step 710. At this stage, all of the Vector 703, tabular 704, andRaster 705 data may then be transformed as steps 711 and 712. An inputfile is then created 713 containing the developed geographic data alongwith specific truncated grid identifier key. The data is then loadedinto the database. The database is then made available 714. The datawith grid identifier is then made available 715 to modeling for analysisto determine predictiveness. The modeling area compares new data againstexisting insurance policy data to determine relevance against aparticular coverage for example. The data is also compared with existingrating data used in models to see if it is a compliment. If the data isdetermined to be predictive, it is kept, otherwise the results arestored and the data is not used. The result is the predictive data 716.The predictive data has been fed 717 into an existing model that is usedto develop factors for rate making The factors 718 are loaded intodatabases. A table is then created/updated 719 that contains thetruncated grid identifier and rating factors.

FIG. 7 c illustrates an example of graphical information to be mappedinto a GRID-based data structure. While the following description refersspecifically to a brush fire risk, the techniques can be applied to anydata element in order to assign specific data values to GRID cells. Theinsurer has been provided with data for states that have exposure toBrush Fire risk. The Brush Fire Risk at a particular location considersseveral factors, including elevation, vegetation, land cover, etc., inaddition to assigned risk values to areas in the state. The insurer hasbeen provided maps of several states that show polygons and associatedBrush Fire risk values. Risk values are 1, 2, 3, and 4. As the dataprovided is not by GRID, but rather by polygons of different sizes andshapes in a graphical information format, this document explains severalmethods to assign polygon risk values to GRID cells. A commongeographical information system (GIS) file format is addressed herein asa non-limiting examples: Geospatial boundary files in GSB format andMapInfo® files.

Data provided from certain sources are converted to GSB files. UserDefined Functions in insurer data stores utilize the GSB files toperform spatial look ups on latitude and longitude values andsubsequently return the risk values associated with the polygon thatcontains the specified latitude and longitude coordinates. A single GRIDcell covers a range of potential latitude and longitude coordinates.Thus, any GRID cell could contain coordinates that fall into one or morepolygons, and thus the algorithm could return one or more risk values.As a result, several approaches have been examined to determine the bestmethod to derive a single risk value for any given GRID cell.

Alternatively, data may be provided in, or converted to, a MapInfo®file, to determine the area covered by each GRID and the associated RiskValues. When multiple polygons span a GRID cell, the GRID cell isassigned the risk value associated with the polygon that is representedby the largest area in the GRID cell. For example, the following mapshows a GRID cell that is spanned by multiple polygons.

In map 700, the grid cell with GRID ID ‘0483010798’ is spanned by sevenpolygons, three with a risk value of 1 (701) and four with a risk valueof 2 (702). The area covered by each polygon spanning GRID ‘0483010798’is given in Table 4.

TABLE 4 Polygon Risk Value Polygon Area 1 2 0.000490051 2 2 0.0002451843 2 0.000245026 4 2 0.003919830 5 1 0.005148140 6 1 0.005148140 7 10.284706000

Thus, upon summing the polygon areas, a risk value of 1 is assigned toGRID ‘0483010798’. An alternative related method could use the riskvalues and the polygon areas to develop a weighted average risk valuefor the GRID cell.

Gather Sufficient Data to Generate Actuarially Credible Results

With rating data stored in association with each GRID cell, the ratingprocess can begin with a data collection means. The process starts atthe target GRID cell, as that data is typically the most relevant to therating process. In many cases, a target GRID cell by itself will nothave adequate experience to produce an actuarially credible result sonearby data will be gathered as needed. According to one embodiment, therating system collects loss/exposure data in ring increments until thequery scope first reaches a maximum distance (e.g., a selected radiusfrom the target GRID) or includes sufficient data to attain maximumcredibility. In an iterative approach, the system makes a determinationfor each GRID cell ring increment as to whether the data gathered isadequate to produce a credible result. The ring is incremented as neededup to the threshold ring distance.

Because most rating calculations will include data from GRID cellssurrounding the target GRID cell, calculations will tend to overlap. Inother words, experience data in a given GRID cell will be used in thecalculations for many nearby GRID cells. This data sharing helps toensure a smoother transition of LRFs across adjacent GRID cells. Incertain embodiments, an exception to this process is that GRID cellsnear certain boundaries (e.g., political or geographical) prevents thesystem from crossing those boundaries to gather data.

This iterative approach is illustrated in FIGS. 3 and 4. FIG. 3illustrates a target GRID cell, a first ring of cells, and a second ringof cells. FIG. 4 shows the stylized town map and overlaid coordinategrid 400 as shown in FIG. 1. Additionally, FIG. 4 shows an overlaidseries of GRID cell rings, according to certain embodiments of thepresent invention. Target GRID cell 401 is located near the center ofthe map. A first ring of GRID cells 402 is located around the targetGRID cell 401. A second ring of GRID cells 403 is located around thefirst ring of GRID cells 402. A third ring of GRID cells 404 is locatedaround the second ring of GRID cells 403. For this particular GRID ringconfiguration, location F resides within the target GRID cell 401. Thus,FIGS. 3 and 4 illustrate how the use of GRID cell ring increments may beused to gather an actuarially credible amount of data needed to rate apolicy in the target GRID cell.

Credibility

This process of aggregating sufficient data to make a reliable andaccurate risk assessment has been discussed above. In certainembodiments, this process is aided by the use of a well-knowncredibility formula, which is:C=ZR+(1−Z)Hwhere:

-   -   R is the mean of the current observations (for example, the        data)    -   H is the prior mean (for example, the estimate based on the        actuary's prior data and/or opinion    -   C is the [insurance rate]    -   Z is the credibility factor, satisfying 0≦Z≦1.        T. Herzog, Credibility: The Bayesian Model Versus Bühlmann's        Model, 41 Transactions of Society of Actuaries 43-88, at        43 (1989) (herein incorporated by reference). The credibility        factor Z is defined by:

$\begin{matrix}{Z = \frac{n}{\left( {n + k} \right)}} & (4.1)\end{matrix}$

-   -   and satisfies 0≦Z≦1; also, n is the number of trials or exposure        units, and

$\begin{matrix}{{k = \frac{{expected}\mspace{14mu}{value}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{process}\mspace{14mu}{variance}}{{variance}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{hypothetical}\mspace{14mu}{means}}}\ldots} & (4.2)\end{matrix}$Herzog, at 53. The basic principle is that the credibility of the rateincreases with quantity n or N_(z) of relevant expected claims orexposure units. The credibility factor may also be calculated as afunction of the expected number of claims (N_(z)) and the fullcredibility standard (N_(f)).

$z = \sqrt{\frac{N_{z}}{N_{f}}}$G. Ventner, “Classical Partial Credibility with Application to Trend,”Proceedings of the Casualty Actuarial Society Casualty Actuarial Societyat 31 (1986) (herein incorporated by reference).Adjust Historical Data for Distance

Because geographically distant data may be less relevant to the ratingprocess than geographically near data, certain embodiments of thepresent disclosure may include a step of applying a distance weightingalgorithm to discount or devalue data as a function of distant from thetarget cell. Thus, the method of these embodiments relies more heavilyon the immediately adjacent GRID cell experience than on distant GRIDcell experience. However, this weighting process may be more relevant tocertain perils or coverages than others.

In the case of automobile insurance, calculations may be carried out oneach major coverage to develop an initial indicated LRF by coverage.These amounts are then credibility weighted to develop the finalindicated LRFs by coverage.

In the case of homeowners insurance, calculations may be carried out oneach peril to develop an expected loss per policy, i.e.—pure premium, byperil. Where insurance companies do not develop Homeowners premiums byperil, an adjustment may be made to the exposure data by peril toaccount for any distributional differences in rating variables across astate or geographic area. The resulting exposure adjusted loss perpolicy amounts are then credibility weighted with an appropriatecomplement of credibility. These peril expected loss per policy amountsare then aggregated to an all peril basis and used to derive the allperil LRFs.

For homeowner policies, special considerations may be warranted. Forexample, due to the potential volatility in Homeowners data (or anyinsurance product line with low exposures), losses may be capped invarious ways to mitigate the impact of shock losses or events. In someembodiments, losses may be capped based on the dollar amount incurred orpaid over a specified period of time. For example, losses incurred orpaid over a day, week, year or any other relevant period of time can beused in the analysis.

In other embodiments, individual claims may be capped at a specifiedthreshold or otherwise removed to limit their impact on the finalindicated LRFs.

In other embodiments, it may further be appropriate to cap actual lossesper policy, i.e., pure premium, at a specified multiple of thecomplement of credibility. Table 5 provides an example.

TABLE 5 Data Item Value Loss $167,090 Common Risk Exposure 10.83 LossPure Premium $15,434.11 Statewide Loss Pure Premium $81.63 Capped LossPure Premium $326.52 Credibility Standard 1,250.00 Claims 4.00Credibility Factor 0.0566 Model Loss Pure Premium 90.20 Relativity11.738 Relativity with Capped Pure Premium 1.269

Losses in the above table and throughout this document may or may notinclude loss expenses and may or may not include losses due tocatastrophic events. Losses and loss expenses may also be on a paid,case incurred, or total incurred basis.

Apply Credibility Weighting

A further step of the process may be to apply credibility weightingusing an appropriate credibility complement. Peril credibility standardsmay be developed based on accepted methodologies. Credibility standardsmay be based on confidence intervals and acceptable error thresholds.

Calculate Pure Premiums

Yet another step of the process may be to calculate pure premiums foreach target GRID cell. For example, expected pure premium may becalculated by peril or coverage and then aggregated to an all-peril orall-coverage basis.

According to an embodiment of the invention, pure premiums may becalculated using a provision that represents a long term average lossper exposure or alternatively be set to another appropriate provision,such as setting them equal to statewide pure premiums. Then, the perilor coverage pure premiums may be aggregated to an all-peril orall-coverage basis.

According to an embodiment of the invention, exposure may be adjusted toaccount for any distributional differences in rating variables across astate or geographic area. Peril or coverage specific adjustment factorsmay be used. An average GRID cell adjustment may be calculated relativeto an average statewide adjustment. For example, Table 6 illustrates anexample process to develop exposure adjustments for two GRIDs.

TABLE 6 Expo- Deductible (1) Construction (2) sure Adjust- Adjust- (1) *GRID Count Value ment Value ment (2) 0369010450 10 $500 1.25 Frame 1.001.2500 0369010450 20 $500 1.25 Ma- 0.85 1.0625 sonry 0369010450 20 1%1.00 Frame 1.00 1.0000 GRID Weighted 1.0750 Average 0369010451 20 $5001.25 Frame 1.00 1.2500 0369010451 10 $500 1.25 Ma- 0.85 1.0625 sonry0369010451 40 $1,000   1.05 Ma- 0.85 0.8925 sonry 0369010451 25 1% 1.00Frame 1.00 1.0000 0369010451 15 2% 0.90 Ma- 0.85 0.7650 sonry GRIDWeighted 0.9800 Average State Weighted 1.0097 AverageThe above example assumes only two GRIDs exist in the state. Table 7provides the weighted adjustment and the exposure adjustments for thetwo illustrative GRIDs of Table 6.

TABLE 7 GRID Weighted Adjustment Peril Exposure Adjustment 03690104501.0750 1.0647 0369010451 0.9800 0.9706 Statewide 1.0097 1.0000Develop Location Rating Factor

A further step of the process may be to develop an indicated LocationRating Factor.

Process Steps

FIG. 8 provides a flow chart, which is illustrative of an embodiment ofthe invention. Flow chart 800 illustrates an example flow of steps takento develop a pure premium. Data is input 801 into a database, so that atable of all the GRID cells for a particular state is maintained in aseparate database table. Other data may also be input into the system tobe used for the analysis, such as grid level data, policy level data,and latitude and longitude level data. The location of the relevantperson, property or thing being insured is then determined 802 and aninitial selection of the corresponding GRID cell is identified.Longitudinal and latitudinal coordinates are then truncated 803depending on how the GRID has been truncated for this area. The ring ofGRID cells is set or initialized 804 to zero, which will result in aquery for data in the target cell. In some embodiments, the ring size isinitialized to one after data has been queried for the target cell.

Data filters are set 805 to restrict the query to relevant data. Filtersmay be incorporated into a database query string, or may be applied tothe query results. Filters may, for example, restrict the query to datarelevant to:

-   -   types of claim, e.g., auto claims;    -   specific perils, e.g., data associated with a fire hazard;    -   types of properties, e.g., single-family residence, commercial        property, or vacation home;    -   classifications of properties, e.g., standard homes, mobile        homes, or high-rise home;    -   features of properties, e.g., construction material, size,        construction cost, waterfront, elevation, or neighborhood        characteristics; or    -   geographic or political subdivisions, e.g., limited to a state,        county, or regulatory district.

The last item listed is of some importance with certain regulatedproducts. Some insurance and financial services products are regulatedby local or regional governmental entities. For example, regulation ofcertain insurance products is performed by state agencies in the UnitedStates. These entities may be referred to as regulators. In someembodiments, the database may contain generalized rules and criteriathat apply to all policies and specific rules associated with aparticular regulator. This arrangement of data would allow, for example,a single computational process to generate a rate for an insurancepolicy for a property in any state.

Data corresponding to the ring of GRID cells is queried 806. To query806 the data, a temporary table of data is created for the data existingat the latitude/longitude level. The temporary data is summarized at thelevel of desired truncation and maintained until the completion of theanalysis, since it is reused in each iteration. A query uses the inputcell and determines via query which of the other cells in the state fallwithin the ordered rings up to the maximum distance, for example, 30miles. Once the data for the other GRID cells are ordered, they areiterated through until a level of credibility is met, using thesummarized data. Query 806 produces a target set of data that isassociated with the target cell and/or current ring.

The target set of data is then processed 807, once the level is met thevalues retrieved and weighted based on the GRID ring, by loading thedata into an overall calculation that develops a value for Ring O. Adetermination 808 is then made as to whether the processed data isgreater than or equal to the maximum credibility or target credibility.If the credibility of the processed data is not greater than or equal tothe maximum credibility or target credibility, then a determination 809is made as to whether size of the ring of GRID cells has reached amaximum. If the size of the ring of GRID cells has not reached amaximum, then the ring size is incremented 810 by one and the processreturns to the query step 806. If the size of the ring of GRID cells hasreached a maximum, then a sparse data handler is used to calculate arate. The process is continued for each GRID cell within a set oflatitude/longitude boundaries, for example, defined by a state. If theprocessed data of step 807 is determined 808 to be greater than or equalto the maximum credibility or target, then a rate is adjusted 812. Insome cases, the resulting rate values created are just a portion of thepotential rate that would be presented to a customer because there aremany factors that may come into play in calculating the final rate.Additional steps may also be added to modify the rate before it iscommunicated to a customer. Finally, a rate may then be communicated 813to the potential customer for whom the policy rate quote has beenrequested.

Sparse data handler 811 provides a mechanism for addressing ratingrequests that cannot be completed normally as a result of insufficientrating data. In certain embodiments, sparse data handler 811 returns anerror message signaling an inability to calculate a pure premium. Insome embodiments, the error message may include the calculated purepremium information along with the credibility factor.

Example

As a further example of one aspect of the invention, specific data isprovided to illustrate a method having the following steps: collectingdata according to GRID rings, determining whether rings have enoughhistorical data to provide actuarially credible results, adjusting thedata for distance, and applying credibility weighting. The calculationsherein represent a rating means, according to certain embodiments of thepresent disclosure.

First, historical data may be collected for GRID ring 0 (target GRID).Table 8 illustrates a data collection for claims and loss and lossexpenses (referred to below as Loss Amount) for an exemplary targetGRID.

TABLE 8 GRID Ring Claims Loss Amount 0 8 $20,640 Total 8 $20,640

Second, historical data may be collected for GRID ring 1. This GRID ringmay be for an interval ring having an inside and outside radius atselected distances from the target GRID cell. Table 9 illustrates a datacollection for claims and loss and loss expenses for the exemplarytarget GRID and the exemplary GRID ring 1.

TABLE 9 GRID Ring Claims Loss Amount 0 8 $20,640 1 23 $64,519 Total 31$85,159Historical data may also be collected for GRID ring 2. This GRID ringmay be for an interval ring having an inside and outside radius atselected distances from the target GRID cell, but lying outside andsurrounding GRID ring 1.

Table 10 illustrates a data collection for claims and loss and lossexpenses for the exemplary target GRID, the exemplary GRID ring 1, andthe exemplary GRID ring 2.

TABLE 10 GRID Ring Claims Loss Amount 0 8 $20,640 1 23 $64,519 2 93$247,717  Total 124 $332,876 Loss and exposure data may be collected for additional interval ringsuntil maximum credibility is obtained or a maximum distance is reached.In this illustrative example, maximum credibility is obtained with datafor only seven GRID rings being collected.

Table 11 illustrates a data collection for claims and loss and lossexpenses for the exemplary target GRID (GRID ring 0) and the exemplaryGRID rings 1-6.

TABLE 11 GRID Ring Claims Loss Amount 0 8  $20,640 1 23  $64,519 2 93$247,717 3 160 $404,077 4 206 $581,755 5 348 $955,226 6 414 $1,095,219  Total 1,252 $3,369,154  While the loss and exposure data above is illustrative for one peril orcoverage, data may be collected in the same manner for any peril orcoverage.

Third, a distance weighting factor may be applied to the loss andexposure data. For this particular example, as shown in FIGS. 9A and10A, the weighting factor is about 1 for GRID rings 0-3 and GRID rings0-8, respectively, and then the weighting factor decreases towards zero.Weighting factors may follow any function or curve shape. The weightingfactor may even be truncated or it may increase with distance from thetarget GRID.

Fourth, credibility weighting may be applied using an external datamodel result, statewide pure premium, prior indicated factor, factorimplied by current rate, or any other relevant value as the complement.Credibility standards may be based on varying confidence intervals andacceptable error thresholds.

Fifth, pure premiums may be calculated by peril for expected loss andloss expenses. The loss and loss expenses may be divided by the exposureto determine the loss and loss expenses pure premium. Illustrativeresults are provided in Table 12 for one peril.

TABLE 12 Data Item Value Loss $818,851.12 Common Risk Exposure $4,280.14Loss Pure Premium $191.31 Capped Loss Pure Premium $191.31 CredibilityStandard 1,250 Claims 313 Credibility Factor 0.50 Model Loss PurePremium $186.29 Expected L&ALAE PP $188.80

An expected loss and loss expenses pure premium may also be calculatedbased on a provision that represents a long term average loss perexposure or alternatively be set to another appropriate provision, suchas setting them equal to statewide pure premiums.

Sixth, pure premiums for the various perils or coverages may beaggregated to an all-peril or all-coverage basis.

Seventh, the all-peril or all-coverage pure premiums can then be used toderive an indicated Location Rating Factor

Example Auto GRID (Geographic Rating ID) Rating Methodology

According to one exemplary application of the invention, an automobileGRID rating methodology is illustrated. The calculations hereinrepresent a rating means, according to certain embodiments of thepresent disclosure. A GRID ring experience is included in thecalculations for a target GRID cell until one of the following criteriais met: (1) reach full or maximum credibility; (2) reach the maximumdistance; or (3) reach maximum change in additional variable (e.g.,percent change in population density). The GRID ring experience may beadjusted based on a distance factor. The distance factor may bedetermined based on a linear or non-linear function of maximum truncateddistance from the target GRID cell. The same distance factor may beapplied to each cell within a given ring.

According to a methodology for the research plan, a GRID cell experiencering analysis may be conducted based on an insurance provider'shistorical data to predict non-catastrophe expected pure premiums bycoverage. An optimization program may evaluate the following by coverageby: (1) maximum credibility assigned to a target cell's experience area;(2) distance weighting functions applied to ring experience; (3) lossexperience period used to develop a target cell's experience and theweight given to each year's experience; and (4) impact of changes inadditional variables.

Data and methodology for this auto example are provided in FIGS. 9A-9F.FIG. 9A provides representative GRID distance weighting values, for anautomobile example. FIG. 9B provides an overview of the methodology foran automobile example. FIG. 9C provides the GRID ring level data for anautomobile example. FIG. 9D provides the GRID cell level data for anautomobile example. FIG. 9E provides methodologies for calculatingdistance between two latitude and longitude coordinate pairs in anautomobile example. FIG. 9F provides the results of the distancecalculations for an automobile example.

Example Homeowners GRID (Geographic Rating ID) Pricing Methodology

According to one exemplary application of the invention, GRID ratingmethodology is illustrated. The calculations herein represent a ratingmeans, according to certain embodiments of the present disclosure. AGRID ring experience is included in the calculations for a target GRIDcell until one of the following criteria is met: (1) reach full ormaximum credibility; (2) reach the maximum distance; or (3) reachmaximum change in additional variable (e.g., percent change inpopulation density). The GRID ring experience may be adjusted based on adistance factor. The distance factor may be determined based on a linearor non-linear function of maximum truncated distance from the targetGRID cell. The same distance factor may be applied to each cell within agiven ring.

According to a methodology research plan, predictive models based onexternal data may be built to predict non-catastrophe expected purepremiums by peril. GRID cell experience ring analysis may be conductedbased on insurer's historical data to predict non-catastrophe expectedpure premiums by peril. An optimization program may evaluate thefollowing by peril: (1) maximum credibility assigned to a target cell'sexperience area; (2) distance weighting functions applied to ringexperience; (3) weight between external data model and ring projectedpure premium; (4) loss experience period used to develop a target cell'sexperience and the weight given to each year's experience; and (5)impact of changes in additional variables. A method may be developed toassign appropriate weights to model and experience based non-catastropheexpected pure premiums by peril. A method may be developed to combinenon-catastrophe expected pure premiums by peril to give all-perilnon-catastrophe expected pure premiums which would then be used tocalculate GRID cell premium relativities.

Data and methodology for this auto example are provided in FIGS.10A-10F. FIG. 10A provides representative GRID distance weighting vales,for a homeowner's policy example. FIG. 10B provides an overview of themethodology for a homeowner's policy example. FIG. 10C provides the GRIDring level data for a homeowner's policy example. FIG. 10D provides theGRID cell level data for a homeowner's policy example. FIG. 10E providesmethodologies for calculating distance between two latitude andlongitude coordinate pairs in a homeowner's example. FIG. 10F providesthe results of the distance calculations for a homeowner's example.

System

FIG. 11 illustrates a computing and information handling systemaccording to one embodiment of the invention. System 1100 comprises oneor more computers 1110. Each computer 1110 may comprise a centralprocessing unit (CPU) 1101, a user interface 1102, a memory 1103, and anetwork interface 1104. The memory 1103 comprises one or moreapplication software modules and one or more internal data stores.System 1100 further comprises a communications network 1105 and externaldata stores 1106.

Computer 1110 may be any type of general purpose or specialized computersystem. In some embodiments computer 1110 may be a personal computer(e.g., an X86-based computer) running a operating system such as UNIX™,OSX™, or WINDOWS™. In some embodiments computer 1110 may be a server orworkgroup class system such as those offered by IBM™, HP™, COMPAQ™, orORACLE™. In other embodiments, computer 1110 may be a mainframe systemsuch as an IBM ZSERIES# mainframe. System 1100 may comprise aheterogeneous or homogeneous network of computers 1110. In someembodiments, computer 1110 may be a mobile device such as a laptop orsmart phone.

CPU 1101 may be any general purpose processor including ARM™, X86, RISC,and Z10™. Memory 1103 may be any form or combination of volatile and/ornon-volatile tangible computer readable medium including semiconductormemory (e.g., RAM, ROM, flash, EEPROM, and MRAM), magnetic memory (e.g.,magnetic hard drives, floppies, and removable drive cartridges), opticalmemory (e.g., CD-ROM, DVD-ROM, BLURAY™ ROM, and holographic storage).Memory 1103 provides transient and/or persistent storage of ApplicationSoftware Modules and Internal data. Memory 1103 also provides storagefor operating system software including device drivers and systemconfigurations. Network interface 1104 provides data interconnection—viacommunications network 1105—between computers 1110 and external data1106.

Internal data may comprise data stored as bitmaps, vectors, objects,tables, and/or files. Internal data may be associated with GRID cells.Internal data may be comprehensive or may be a subset of data limited toa particular geographical region. In some embodiments, internal data mayinclude a limited set of GRID cell data to allow an agent to performrating operations using a mobile device (e.g., a mobile device runningIOS™, ANDROID™, PALMOS™, or WINDOWS™). The data set may be limited toGRID cells in a given metropolitan area, for example. In someembodiments, internal data may be limited to one or more states where anagent is licensed to write policies. In some embodiments, internal datamay include a limited set of GRID cell data to allow an agent to rateonly certain predetermined perils (e.g., auto and homeowners, but notcommercial fire).

Application Software modules comprise software or firmware instructionsand configuration information that provides instructions to CPU 1101 toperform the steps of the methods, procedures, and functions disclosedherein. Application Software may be implemented in a compiled and/orinterpreted environment. In some embodiments, Application Softwaremodules may be implemented in a high-level programming language such asCOBOL, FORTRAN, C, C++, SmallTalk, JAVA™, C#, assembly language, JAVA™server pages (JSP), application server pages (ASP), or VISUAL BASIC™.

Communications network 1105 may be a heterogeneous or homogenous set ofphysical mediums (e.g., optical fiber, radio links, and copper wires)and protocol stacks (e.g., ETHERNET™, FDDI, GSM, WIMAX™, LTE, USB™,BLUETOOTH™, FIOS™, 802.11, and TCP/IP.

External data 1106 may be any form of data source. In some embodiments,external data 1106 is received on an optical disk and imported into aninternal data store for further processing. In some embodiments,external data 1106 is an external data store hosted on a computeraccessible via communications network 1105. External data 1106 may beavailable for on demand retrieval or may be pushed by a data provider.External data 1106 may be transferred to computer 1110 in whole or inpart. This transfer may be periodic, on demand, or as changes occur.

System 1100 may be configured such that one or more of the computers1110 is configured with hardware and software that enables it to collectinternal and/or external data and map that data into an internal datastore associated with GRID cell identifiers. One or more computers 1110are further configured to collect data for each target GRID cell and itsadjacent GRID rings, determine whether GRID rings have enough historicaldata to provide actuarially credible results for each target GRID cell,adjust the data of the GRID rings for distance, apply credibilityweighting, and calculate pure premiums for each GRID cell.

The system 1100 is configured such that one or more of the computers1110 is configured to receive an input request through a user interface1102 for a rate quote for an insurance policy covering a risk associatedwith a particular location. The computer 1110 first obtains the latitudeand longitude coordinates for the location via direct input (e.g., viauser interface 1102), an address cross-reference table (e.g., ageolocation database), direct GPS input, etc. The computer 1110 thenexecutes a look-up process whereby the latitude and longitudecoordinates for the location are used to identify a target GRID cell inthe GRID cell network. The computer 1110 then generates pure premiumsfor the target GRID cell and returns a rate quote for the location.

For the purposes of this disclosure, the term exemplary means exampleonly. Although the disclosed embodiments are described in detail in thepresent disclosure, it should be understood that various changes,substitutions and alterations can be made to the embodiments withoutdeparting from their spirit and scope.

What is claimed is:
 1. A non-transitory computer readable mediumcontaining a set of computer readable instructions for determining abase rate for a requested hazard insurance policy that when loaded intoa computer configure that computer to: receive demographic data, firestation data, or other risk-assessment data associated with a locationassociated with the requested hazard insurance policy; receive acoordinate pair including a longitude and latitude of the location;determine a coordinate grid block bounded by latitude and longitudelines, which grid block is associated with the coordinate pair and has asize determined at least in part by attributes of the location; query adatabase for a first plurality of existing data associated with thecoordinate grid block; automatically determine that the quantity ofexisting data associated with the coordinate grid block is below athreshold level; in response to determining that the quantity ofexisting data associated with the coordinate grid block is below thethreshold level, define a current ring of coordinate grid blockssurrounding the coordinate grid block; query the database for a secondplurality of existing data associated with the current ring ofcoordinate grid blocks; determine a base rate for the requested hazardinsurance policy based at least in part on the first and secondpluralities of existing data and the demographic data, fire stationdata, or other data associated with a location; and transmit thedetermined base rate to a user.
 2. The non-transitory computer readablemedium containing a set of computer readable instructions as claimed inclaim 1, that when loaded into a computer further configure thatcomputer to: determine a number of latitudinal truncation digits and anumber of longitudinal truncation digits defining the size of thecoordinate grid block based at least in part on attributes of thelocation; determine a longitude query value by truncating the longitudeof the coordinate pair based on the number of longitudinal truncationdigits; determine a latitude query value by truncating the latitude ofthe coordinate pair based on the number of latitudinal truncationdigits; and transmit the longitude query value and latitude query valueto the database to query the database.
 3. The non-transitory computerreadable medium containing a set of computer readable instructions asclaimed in claim 1, that when loaded into a computer further configurethat computer, wherein the step to determine a base rate implements arate making formula associated with a regulated geographical areasubject to regulation of hazard insurance policies wherein the solesource of variable input to the rate making formula is a set of dataelements, each of which is associated with one specific coordinate gridblock.
 4. The non-transitory computer readable medium containing a setof computer readable instructions as claimed in claim 1, that whenloaded into a computer further configure that computer to: determine agrid block size based at least in part on one of: a peril type, aquantity of experience data present in the database and associated withthe target coordinate grid block, a population density associated withthe coordinate pair, and a political subdivision associated with thecoordinate pair.
 5. The non-transitory computer readable mediumcontaining a set of computer readable instructions as claimed in claim1, that when loaded into a computer further configure that computer to:determine an attribute representative of a property at the locationindicated by the coordinate pair; select one data filter associated withthe representative attribute from a plurality of data filters; and applythe selected one data filter to the first and second pluralities ofexisting data.
 6. The non-transitory computer readable medium containinga set of computer readable instructions as claimed in claim 1, that whenloaded into a computer further configure that computer to, prior toreceiving the coordinate pair: receive a data value that is to beassociated with more than one coordinate grid block; store in thedatabase a first record including information relating to the data valueand associated with a first grid block; and store in the database asecond record including information relating to the data value andassociated with a second grid block.
 7. The non-transitory computerreadable medium containing a set of computer readable instructions asclaimed in claim 1, wherein the first plurality of existing dataassociated with the coordinate grid block includes a shared data elementthat is associated with more than one coordinate grid blocks.